2_how_to_use_trainer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let’s try using the Trainer feature\n",
    "\n",
    "By using [Trainer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Trainer.html#chainer.training.Trainer), you don’t need to write the tedious training loop explicitly any more. Furthermore, Chainer provides many useful extensions that can be used with [Trainer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Trainer.html#chainer.training.Trainer) to visualize your results, evaluate your model, store and manage log files more easily.\n",
    "\n",
    "This example will show how to use the [Trainer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Trainer.html#chainer.training.Trainer) to train a fully-connected feed-forward neural network on the MNIST dataset.\n",
    "\n",
    "**Note**\n",
    "\n",
    "If you would like to know how to write a training loop without using this functionality, please check [How to write a training loop in Chainer](training_loop_in_chainer.ipynb) instead of this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's import the necessary packages for using Chainer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import chainer\n",
    "from chainer import cuda, Function, gradient_check, report, training, utils, Variable\n",
    "from chainer import datasets, iterators, optimizers, serializers\n",
    "from chainer import Link, Chain, ChainList\n",
    "import chainer.functions as F\n",
    "import chainer.links as L\n",
    "from chainer.training import extensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Prepare the dataset\n",
    "\n",
    "Load the MNIST dataset, which contains a training set of images and class labels as well as a corresponding test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from chainer.datasets import mnist\n",
    "\n",
    "train, test = mnist.get_mnist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "You can use a **Python list as a dataset**. Because all types of objects whose element can be accessed via `[]` accessor and lengh can be obtained with `len()` function, can be used as a dataset given to the [Iterator](https://docs.chainer.org/en/latest/reference/core/generated/chainer.dataset.Iterator.html#chainer.dataset.Iterator). For example,\n",
    "\n",
    "```\n",
    "train = [(x1, t1), (x2, t2), ...]\n",
    "```\n",
    "\n",
    "a list of tuples like this can also be used equally to a [DatasetMixin](https://docs.chainer.org/en/latest/reference/core/generated/chainer.dataset.DatasetMixin.html#chainer.dataset.DatasetMixin) object.\n",
    "\n",
    "But many useful abstracted [datasets](https://docs.chainer.org/en/latest/reference/datasets.html#module-chainer.datasets) enable to avoid storing all data on the memory at a time, so it’s better to use them for large datasets. For example, [ImageDataset](https://docs.chainer.org/en/latest/reference/generated/chainer.datasets.ImageDataset.html#chainer.datasets.ImageDataset) takes paths to image files as its argument, and just keep the list in the dataset object. It means the actual image data will be loaded from disks using given paths when [\\_\\_getitem\\_\\_()](https://docs.chainer.org/en/latest/reference/generated/chainer.datasets.ImageDataset.html#chainer.datasets.ImageDataset.__getitem__) is called. Until then, no images are loaded to the memory, so it can save the memory consumption."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Prepare the dataset iterations\n",
    "\n",
    "[Iterator](https://docs.chainer.org/en/latest/reference/core/generated/chainer.dataset.Iterator.html#chainer.dataset.Iterator) creates a mini-batch from the given dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batchsize = 128\n",
    "\n",
    "train_iter = iterators.SerialIterator(train, batchsize)\n",
    "test_iter = iterators.SerialIterator(test, batchsize, False, False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Prepare the model\n",
    "\n",
    "Here, we are going to use the same model as defined in [How to write a training loop in Chainer](1_training_loop_in_chainer.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MLP(Chain):\n",
    "\n",
    "    def __init__(self, n_mid_units=100, n_out=10):\n",
    "        super(MLP, self).__init__()\n",
    "        with self.init_scope():\n",
    "            self.l1 = L.Linear(None, n_mid_units)\n",
    "            self.l2 = L.Linear(None, n_mid_units)\n",
    "            self.l3 = L.Linear(None, n_out)\n",
    "\n",
    "    def __call__(self, x):\n",
    "        h1 = F.relu(self.l1(x))\n",
    "        h2 = F.relu(self.l2(h1))\n",
    "        return self.l3(h2)\n",
    "\n",
    "model = MLP()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Prepare the Updater\n",
    "\n",
    "[Trainer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Trainer.html#chainer.training.Trainer) is a class that holds all of the necessary components needed for training. The main components are shown below.\n",
    "\n",
    "![](trainer.png)\n",
    "\n",
    "Basically, all you need to pass to [Trainer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Trainer.html#chainer.training.Trainer) is an [Updater](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Updater.html#chainer.training.Updater). However, [Updater](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Updater.html#chainer.training.Updater) contains an [Iterator](https://docs.chainer.org/en/latest/reference/core/generated/chainer.dataset.Iterator.html#chainer.dataset.Iterator) and [Optimizer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.Optimizer.html#chainer.Optimizer). Since [Iterator](https://docs.chainer.org/en/latest/reference/core/generated/chainer.dataset.Iterator.html#chainer.dataset.Iterator) can access the dataset and [Optimizer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.Optimizer.html#chainer.Optimizer) has references to the model, [Updater](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Updater.html#chainer.training.Updater) can access to the model to update its parameters.\n",
    "\n",
    "So, [Updater](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Updater.html#chainer.training.Updater) can perform the training procedure as shown below:\n",
    "\n",
    "Retrieve the data from dataset and construct a mini-batch ([Iterator](https://docs.chainer.org/en/latest/reference/core/generated/chainer.dataset.Iterator.html#chainer.dataset.Iterator))\n",
    "Pass the mini-batch to the model and calculate the loss\n",
    "Update the parameters of the model ([Optimizer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.Optimizer.html#chainer.Optimizer))\n",
    "\n",
    "Now let’s create the [Updater](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Updater.html#chainer.training.Updater) object !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "max_epoch = 10\n",
    "\n",
    "# Wrapp your model by Classifier and include the process of loss calculation within your model.\n",
    "# Since we do not specify a loss funciton here, the default 'softmax_cross_entropy' is used.\n",
    "model = L.Classifier(model)\n",
    "\n",
    "gpu_id = 0  # Set to -1 if you use CPU\n",
    "if gpu_id >= 0:\n",
    "    model.to_gpu(gpu_id)  # If you use CPU, comment out this line\n",
    "\n",
    "# selection of your optimizing method\n",
    "optimizer = optimizers.MomentumSGD()\n",
    "\n",
    "# Give the optimizer a reference to the model\n",
    "optimizer.setup(model)\n",
    "\n",
    "# Get an updater that uses the Iterator and Optimizer\n",
    "updater = training.StandardUpdater(train_iter, optimizer, device=gpu_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**\n",
    "\n",
    "Here, the model defined above is passed to [Classifier](https://docs.chainer.org/en/latest/reference/generated/chainer.links.Classifier.html#chainer.links.Classifier) and changed to a new [Chain](https://docs.chainer.org/en/latest/reference/core/generated/chainer.Chain.html#chainer.Chain). [Classifier](https://docs.chainer.org/en/latest/reference/generated/chainer.links.Classifier.html#chainer.links.Classifier), which in fact inherits from the [Chain](https://docs.chainer.org/en/latest/reference/core/generated/chainer.Chain.html#chainer.Chain) class, keeps the given [Chain](https://docs.chainer.org/en/latest/reference/core/generated/chainer.Chain.html#chainer.Chain) model in its `predictor` attribute. Once you give the input data and the corresponding class labels to the model by the `()` accessor,\n",
    "\n",
    "1. [\\_\\_call\\_\\_()](https://docs.chainer.org/en/latest/reference/generated/chainer.links.Classifier.html#chainer.links.Classifier.__call__) of the model is invoked. The data is then given to `predictor` to obtain the output `y`.\n",
    "2. Next, together with the given labels, the output `y` is passed to the loss function which is determined by `lossfun` argument in the constructor of [Classifier](https://docs.chainer.org/en/latest/reference/generated/chainer.links.Classifier.html#chainer.links.Classifier).\n",
    "The loss is returned as a [Variable](https://docs.chainer.org/en/latest/reference/core/generated/chainer.Variable.html#chainer.Variable).\n",
    "\n",
    "In [Classifiler](https://docs.chainer.org/en/latest/reference/generated/chainer.links.Classifier.html#chainer.links.Classifier), the `lossfun` is set to [softmax_cross_entropy()](https://docs.chainer.org/en/latest/reference/generated/chainer.functions.softmax_cross_entropy.html#chainer.functions.softmax_cross_entropy) as default.\n",
    "\n",
    "[StandardUpdater](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.StandardUpdater.html#chainer.training.StandardUpdater) is the simplest class among several updaters. There are also the [ParallelUpdater](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.ParallelUpdater.html#chainer.training.ParallelUpdater) and the [MultiprocessParallelUpdater](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.updaters.MultiprocessParallelUpdater.html#chainer.training.updaters.MultiprocessParallelUpdater) to utilize multiple GPUs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Setup Trainer\n",
    "\n",
    "Lastly, we will setup [Trainer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Trainer.html#chainer.training.Trainer). The only requirement for creating a [Trainer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Trainer.html#chainer.training.Trainer) is to pass the [Updater](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Updater.html#chainer.training.Updater) object that we previously created above. You can also pass a stop_trigger to the second trainer argument as a tuple like `(length, unit)` to tell the trainer when to stop the training. The `length` is given as an integer and the `unit` is given as a string which should be either `'epoch'` or `'iteration'`. Without setting `stop_trigger`, the training will never be stopped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Setup a Trainer\n",
    "trainer = training.Trainer(updater, (max_epoch, 'epoch'), out='mnist_result')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `out` argument specifies an output directory used to save the log files, the image files of plots to show the time progress of loss, accuracy, etc. when you use [PlotReport](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.PlotReport.html#chainer.training.extensions.PlotReport) extension. Next, we will explain how to display or save those information by using trainer [Extension](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Extension.html#chainer.training.Extension)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Add Extensions to the Trainer object\n",
    "\n",
    "The [Trainer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Trainer.html#chainer.training.Trainer) extensions provide the following capabilites:\n",
    "\n",
    "- Save log files automatically ([LogReport](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.LogReport.html#chainer.training.extensions.LogReport))\n",
    "- Display the training information to the terminal periodically ([PrintReport](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.PrintReport.html#chainer.training.extensions.PrintReport))\n",
    "- Visualize the loss progress by plottig a graph periodically and save it as an image file ([PlotReport](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.PlotReport.html#chainer.training.extensions.PlotReport))\n",
    "- Automatically serialize the state periodically ([snapshot()](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.snapshot.html#chainer.training.extensions.snapshot) / [snapshot_object()](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.snapshot_object.html#chainer.training.extensions.snapshot_object))\n",
    "- Display a progress bar to the terminal to show the progress of training ([ProgressBar](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.ProgressBar.html#chainer.training.extensions.ProgressBar))\n",
    "- Save the model architechture as a Graphviz’s dot file ([dump_graph](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.dump_graph.html#chainer.training.extensions.dump_graph))\n",
    "\n",
    "To use these wide variety of tools for your tarining task, pass [Extension](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Extension.html#chainer.training.Extension) objects to the [extend()](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Trainer.html#chainer.training.Trainer.extend) method of your [Trainer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Trainer.html#chainer.training.Trainer) object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainer.extend(extensions.LogReport())\n",
    "trainer.extend(extensions.snapshot(filename='snapshot_epoch-{.updater.epoch}'))\n",
    "trainer.extend(extensions.snapshot_object(model.predictor, filename='model_epoch-{.updater.epoch}'))\n",
    "trainer.extend(extensions.Evaluator(test_iter, model, device=gpu_id))\n",
    "trainer.extend(extensions.PrintReport(['epoch', 'main/loss', 'main/accuracy', 'validation/main/loss', 'validation/main/accuracy', 'elapsed_time']))\n",
    "trainer.extend(extensions.PlotReport(['main/loss', 'validation/main/loss'], x_key='epoch', file_name='loss.png'))\n",
    "trainer.extend(extensions.PlotReport(['main/accuracy', 'validation/main/accuracy'], x_key='epoch', file_name='accuracy.png'))\n",
    "trainer.extend(extensions.dump_graph('main/loss'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [LogReport](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.LogReport.html#chainer.training.extensions.LogReport)\n",
    "\n",
    "Collect `loss` and `accuracy` automatically every `'epoch'` or `'iteration'` and store the information under the `log` file in the directory specified by the `out` argument when you create a [Trainer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Trainer.html#chainer.training.Trainer) object.\n",
    "\n",
    "## [snapshot()](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.snapshot.html#chainer.training.extensions.snapshot)\n",
    "\n",
    "The [snapshot()](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.snapshot.html#chainer.training.extensions.snapshot) method saves the [Trainer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Trainer.html#chainer.training.Trainer) object at the designated timing (defaut: every epoch) in the directory specified by `out`. The [Trainer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Trainer.html#chainer.training.Trainer) object, as mentioned before, has an [Updater](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Updater.html#chainer.training.Updater) which contains an [Optimizer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.Optimizer.html#chainer.Optimizer) and a model inside. Therefore, as long as you have the snapshot file, you can use it to come back to the training or make inferences using the previously trained model later.\n",
    "\n",
    "## [snapshot_object()](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.snapshot_object.html#chainer.training.extensions.snapshot_object)\n",
    "\n",
    "By using this, you can save the particular object (for example, the model object wrapped by [Classifier](https://docs.chainer.org/en/latest/reference/generated/chainer.links.Classifier.html#chainer.links.Classifier)) as a separeted snapshot. [Classifier](https://docs.chainer.org/en/latest/reference/generated/chainer.links.Classifier.html#chainer.links.Classifier) is a [Chain](https://docs.chainer.org/en/latest/reference/core/generated/chainer.Chain.html#chainer.Chain) object which keeps the model that is also a [Chain](https://docs.chainer.org/en/latest/reference/core/generated/chainer.Chain.html#chainer.Chain) object as its `predictor` property, and all the parameters are under the `predictor`, so taking the snapshot of `predictor` is enough to keep all the trained parameters basically.\n",
    "\n",
    "## [dump_graph()](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.dump_graph.html#chainer.training.extensions.dump_graph)\n",
    "\n",
    "This method save the structure of the computational graph of the model. The graph is saved in the [Graphviz](http://www.graphviz.org/)'s `dot` format. The output location (directory) to save the graph is set by the `out` argument of [Trainer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Trainer.html#chainer.training.Trainer).\n",
    "\n",
    "## [Evaluator](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.Evaluator.html#chainer.training.extensions.Evaluator)\n",
    "\n",
    "The [Iterator](https://docs.chainer.org/en/latest/reference/core/generated/chainer.dataset.Iterator.html#chainer.dataset.Iterator) that uses the evaluation dataset and the model object are required to use [Evaluator](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.Evaluator.html#chainer.training.extensions.Evaluator). It evaluates the model using the given dataset (typically it’s a validation dataset) at the specified timing interval.\n",
    "\n",
    "## [PrintReport](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.PrintReport.html#chainer.training.extensions.PrintReport)\n",
    "\n",
    "It outputs the spcified values to the standard output.\n",
    "\n",
    "## [PlotReport](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.PlotReport.html#chainer.training.extensions.PlotReport)\n",
    "\n",
    "[PlotReport](https://docs.chainer.org/en/latest/reference/generated/chainer.training.extensions.PlotReport.html#chainer.training.extensions.PlotReport) plots the values specified by its arguments saves it as a image file which has the same naem as the `file_name` argument.\n",
    "\n",
    "---\n",
    "\n",
    "Each [Extension](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Extension.html#chainer.training.Extension) class has different options and some extensions are not mentioned here. And one of other important feature is, for instance, by using the [trigger](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Extension.html#chainer.training.Extension.trigger) option, you can set individual timings to fire the [Extension](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Extension.html#chainer.training.Extension). \n",
    "\n",
    "**To know more details of all extensions, please take a look at the official document: [Trainer extensions](https://docs.chainer.org/en/stable/reference/extensions.html)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Start Training\n",
    "\n",
    "Just call `run()` method from [Trainer](https://docs.chainer.org/en/latest/reference/core/generated/chainer.training.Trainer.html#chainer.training.Trainer) object to start training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch       main/loss   main/accuracy  validation/main/loss  validation/main/accuracy  elapsed_time\n",
      "\u001b[J1           0.55347     0.842984       0.252119              0.929589                  2.52464       \n",
      "\u001b[J2           0.230404    0.934168       0.191109              0.943829                  4.90972       \n",
      "\u001b[J3           0.177264    0.948244       0.154901              0.955004                  7.337         \n",
      "\u001b[J4           0.144268    0.958434       0.128025              0.963311                  9.79845       \n",
      "\u001b[J5           0.120891    0.964769       0.116612              0.964992                  12.2102       \n",
      "\u001b[J6           0.102954    0.970699       0.104901              0.967366                  14.6425       \n",
      "\u001b[J7           0.0905815   0.973181       0.099298              0.970134                  17.1196       \n",
      "\u001b[J8           0.0801042   0.97688        0.0954406             0.970926                  19.6154       \n",
      "\u001b[J9           0.0710973   0.979261       0.0910335             0.972903                  22.0251       \n",
      "\u001b[J10          0.0643986   0.981393       0.0852588             0.974189                  24.4939       \n"
     ]
    }
   ],
   "source": [
    "trainer.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s see the plot of loss progress saved in the mnist_result directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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P2Wxm3LhxzsdxcXGsXbuWjz76iDvvvJOQkBCCgoLIz88nMjLyotd64403iImJ4fXXX8dg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\n/xuH5F4oOcI8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('mnist_result/loss.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about the accuracy?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VBFwp3TFXeWSnQDZQ2ThOP11QEXxewzAfA34FSodvv4JYi2sb2uTm+1rk5ube9DGEqCnx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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('mnist_result/accuracy.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Furthermore, let’s visualize the computaional graph saved with dump_graph() using Graphviz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "dot -Tpng mnist_result/cg.dot -o mnist_result/cg.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zyfmdFAoF4uPjERERgcjISJw8eRIlJSUYMGAAfHx84OfnBz8/PwQEBLQ6t5RI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\nmdEhAAAAAOi3/w+XBmI1ka+B3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('mnist_result/cg.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the top to the bottom, you can see the data flow in the computational graph. It basically shows how data and parameters are passed to the [Function](https://docs.chainer.org/en/latest/reference/core/generated/chainer.Function.html#chainer.Function)s."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Evaluate a pre-trained model\n",
    "\n",
    "Evaluation using the snapshot of a model is as easy as what explained in the tutorial/train_loop.rst."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: 7\n",
      "predicted_label: 7\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model = MLP()\n",
    "serializers.load_npz('mnist_result/model_epoch-10', model)\n",
    "\n",
    "# Show the output\n",
    "x, t = test[0]\n",
    "plt.imshow(x.reshape(28, 28), cmap='gray')\n",
    "plt.show()\n",
    "print('label:', t)\n",
    "\n",
    "y = model(x[None, ...])\n",
    "\n",
    "print('predicted_label:', y.data.argmax(axis=1)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The prediction looks correct. Yatta!**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}